Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordings.

ABSTRACT: Automated seizure detection in long-term video-EEG recordings is far from being integrated into common clinical practice. Here, we leverage classical and state-of-the-art complexity measures to robustly and automatically detect seizures from scalp recordings. Brain activity is scored through eight features, encompassing traditional time domain and novel measures of recurrence. A binary classification algorithm tailored to treat unbalanced dataset is used to determine whether a time window is ictal or non-ictal from its features. The application of the algorithm on a cohort of ten adult patients with focal refractory epilepsy indicates sensitivity, specificity, and accuracy of 90%, along with a true alarm rate of 95% and less than four false alarms per day. The proposed approach emphasizes

SUBMITTER: Ruiz Marin M 

PROVIDER: S-EPMC7811137 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature